[Objective] To address power flow congestion and cost allocation problems in distribution networks caused by peer-to-peer （P2P） energy trading among multiple microgrids （MG）， and to promote the local consumption of distributed energy resources， a coordinated scheduling model considering dynamic network usage fees and power flow congestion is proposed. [Methods] First， a quantitative method for determining dynamic network usage fees is developed. The proposed fee consists of an electrical distance cost， a voltage management cost， and a network loss cost， thereby accounting for both the network resources occupied by P2P transactions and their effects on distribution network operation. Second， a bilevel coordinated scheduling framework involving the distribution system operator （DSO） and multiple MGs is established. At the upper level， the DSO performs power flow security verification for the initial P2P transaction schedules submitted by the MGs. When line power flow or nodal voltage limits are violated， a congestion management mechanism that minimizes the penalty associated with transaction curtailment is implemented to adjust the P2P transactions. At the lower level， a multi-MG scheduling model incorporating the dynamic network usage fees is formulated to coordinate internal resource scheduling and energy transactions among the MGs. Finally， the Karush-Kuhn-Tucker conditions， strong duality theory， and the Fortuny-Amat transformation are employed to reformulate the bilevel model as a tractable single-level mixed-integer second-order cone programming problem. [Results] Simulation studies based on a modified IEEE 33-bus system demonstrate that the proposed model can promote the local consumption of distributed energy resources， reduce distribution network losses and overall social costs， and effectively mitigate nodal voltage limit violations. The dynamic network usage fees can reasonably reflect the effects of P2P energy trading among multiple MGs on distribution network operation. [Conclusions] The proposed coordinated scheduling model therefore improves the economic efficiency of multi-MG energy trading while maintaining the secure operation of the distribution network.